Bibliographic record
Abstract
This heated argument between a brother and sister after the detective’s visit following the murder of a neighbor and her lover goes to the heart of issues about identity and monstrosity that are explored in Kerri Sakamoto’s The Electrical Field. In this discussion about contemporary Canadian literary traditions and the revision of national and cultural identities through fiction by racial minority writers, what might be the function of images of monstrosity in a novel written by a young Japanese Canadian woman in the late 1990s? Are they confined to the accusations hurled in a family quarrel? On the contrary, as I shall argue, the monster image has a wider resonance here, for this is a story that returns to a deliberately forgotten episode in Canada’s recent past that casts its uncanny shadows over the present. This novel takes as its subject the psychological legacy of the internment of Japanese Canadians in World War II after Pearl Harbor, which was undergone by Sakamoto’s parents and relatives and was first written about in fiction by Joy Kogawa in her novel Obasan (1981). Unlike Kogawa who was interned as a child, Sakamoto born fourteen years after the war looks back to an experience of which she saw only the aftermath. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".